Integrating oral and social factors in individual caries risk assessments in preschool children—a registry-based study
Bibliographic record
Abstract
PURPOSE: To investigate the predictive ability of individual Caries Risk Assessments (CRA) regarding oral factors supplemented with social factors in relation to caries outcome in preschool children. Furthermore, to assess various models of CRA with oral and social factors included, aiming to identify the most suitable models for different age groups. METHODS: The design is a retrospective registry-based cohort study. Children visiting the dentists at ages 3 and 6 years were included. Data on oral and social factors were obtained from dental records, the Swedish Quality register for caries and periodontitis (SKaPa), and Statistics Sweden (SCB). Various models of CRA were designed, combining oral and social factors. Models were analyzed with univariable associations using simple logistic regression, and the results were presented as odds ratios (ORs). In addition, models were analyzed with area under the receiver operating characteristic (ROC) curve (AUC). Pairwise comparisons were conducted by DeLong's test, with p < 0.05 considered significant. RESULT: Oral factors were the most significant for caries outcome (OR 9.6), followed by social factors: foreign background (OR 4.6), low income (OR 2.83), low education of the mother (OR 2.77), single-parent family (OR 2.11), and having ≥ 3 siblings (OR 1.71), (p < 0.01). The predictive ability of CRA improved when models combining oral and social factors were used, compaired to CRA based solely on oral factors (p < 0.05). An increase of up to 15% was seen when CRA was conducted closer to the outcome. CONCLUSION: Models for Caries Risk Assessment including oral and social factors increase the predictive ability. Caries Risk Assessment has limited durability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".